The Red Hat Blog: Expert insights for navigating tech complexity

Moving AI from experimentation to enterprise reality requires a collaborative ecosystem of academia, startups, and open source communities built on transparent, hardware-agnostic standards. By "keeping the hood open," Red Hat aims to provide the architectural expertise and stable foundations necessary for startups to scale while avoiding proprietary lock-in.

Explore the new risks and challenges of AI security, learn about potential attack vectors, and discover how to defend your AI systems with guardrails. This article is the first in a series on AI security.

Red Hat is contributing llm-d to the Cloud Native Computing Foundation (CNCF) as a Sandbox project to standardize high-performance, distributed AI inference serving within the cloud-native stack. This contribution aims to bridge the capabilities gap between AI experimentation and production by providing a specialized data-plane orchestration layer that maximizes infrastructure efficiency and enables flexible deployment on any choice of hardware.

NAIRR, a national program led by the NSF, is democratizing access to high-end AI compute for researchers by creating a shared national infrastructure. Red Hat provides an open source foundation, including OpenShift and OpenShift AI, to serve as the control plane for this infrastructure, helping to maintain project-level boundaries, research isolation, and digital sovereignty.

Learn how to manage AI/ML data with version control using Red Hat OpenShift, Red Hat OpenShift AI, and lakeFS in this quickstart. Improve reproducibility, reduce errors, and scale your AI operations.